Differential Evolution Based Reliable Cooperative Spectrum Sensing in the Presence of Malicious Users

Abstract

In Cognitive Radio Network, access to the vacant spectrum holes in the licensed Primary User (PU) channel is allowed to the Secondary Users. In order to use the PU spectral holes accurately and on time, spectrum sensing is extremely important to avoid interference with the PU transmission. In Cooperative Spectrum Sensing (CSS) individual users report the Fusion Center (FC) about sensing data for a global decision which leads to better sensing results as compared to non-cooperative sensing. The objective of this paper is to make the CSS performance precise and accurate in the presence of Malicious Users (MUs) reporting false sensing information to the FC. The proposed scheme reduces falsifying effects of the YES, NO and OPPOSITE categories of MUs in CSS using Differential Evolution (DE) algorithm. A dynamic threshold value is determined using optimum coefficient vector by the DE at the FC. This leads to minimum error probability in detecting PU channel when the YES, NO and OPPOSITE categories of MUs reports FC in CSS. The weighting coefficient vector is further employed at the FC to assign weights to the received sensing information of cooperative users. Therefore, cooperative users with minutely erroneous reports receive high weight as compared to MUs. The simulations performed for varying number of MUs, total cooperative users, Signal to Noise Ratios and sensing samples show minimum error in detecting PU activity for the proposed DE technique in comparison with the Kullback–Leibler divergence, particle swarm optimization, Genetic Algorithm and Maximum Gain Combination schemes.